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Open Access Research

Joint tests for quantitative trait loci in experimental crosses

T Mark Beasley1*, Dongyan Yang1, Nengjun Yi1, Daniel C Bullard2, Elizabeth L Travis3, Christopher I Amos4, Shizhong Xu5 and David B Allison16

Author Affiliations

1 Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL, USA

2 Department of Genomics and Pathobiology, University of Alabama at Birmingham, Birmingham, AL, USA

3 Department of Experimental Radiation Oncology, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA

4 Department of Epidemiology, University of Texas, M.D. Anderson Cancer Center Houston, TX, USA

5 University of California, Riverside, CA, USA

6 Clinical Nutrition Research Center, University of Alabama at Birmingham, Birmingham, AL, USA

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Genetics Selection Evolution 2004, 36:601-619  doi:10.1186/1297-9686-36-6-601

The electronic version of this article is the complete one and can be found online at: http://www.gsejournal.org/content/36/6/601


Received:16 February 2004
Accepted:24 May 2004
Published:15 November 2004

© 2004 INRA, EDP Sciences

Abstract

Selective genotyping is common because it can increase the expected correlation between QTL genotype and phenotype and thus increase the statistical power of linkage tests (i.e., regression-based tests). Linkage can also be tested by assessing whether the marginal genotypic distribution conforms to its expectation, a marginal-based test. We developed a class of joint tests that, by constraining intercepts in regression-based analyses, capitalize on the information available in both regression-based and marginal-based tests. We simulated data corresponding to the null hypothesis of no QTL effect and the alternative of some QTL effect at the locus for a backcross and an F2 intercross between inbred strains. Regression-based and marginal-based tests were compared to corresponding joint tests. We studied the effects of random sampling, selective sampling from a single tail of the phenotypic distribution, and selective sampling from both tails of the phenotypic distribution. Joint tests were nearly as powerful as all competing alternatives for random sampling and two-tailed selection under both backcross and F2 intercross situations. Joint tests were generally more powerful for one-tailed selection under both backcross and F2 intercross situations. However, joint tests cannot be recommended for one-tailed selective genotyping if segregation distortion is suspected.

Keywords:
joint tests; quantitative trait loci; linkage; F2 cross; backcross

Research

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